Data Visualization

CORH 205

Mathew Vis-Dunbar

March 2023

Overview

What is Data Visualization?

What is Data Visualization?

Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.

What is Data Visualization?

Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.

This involves abstraction as shapes, colours etc are used represent the data.

What is Data Visualization?

Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.

This involves abstraction as shapes, colours etc are used represent the data.

Visual and data literacies are needed to interpret both the data and the abstraction.

Why Do We Visualize Data?

A Definition

Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication.

Stephen Few. Data Visualization for Human Perception.

Attributes & Perception

Even though an object as a whole might take some conscious effort to identify, the basic visual attributes that combine to make up that object are perceived without any conscious effort.

Stephen Few (2004). Tapping the Power of Visual Perception.

Preattentive Attributes

Form

Colour

Position

Preattentive Attributes

Types of Visualisations

Categorical Data

Nominal data

No order

Ordinal data

Intrinsic order

Categorical Data

Nominal data

No order

Ordinal data

Intrinsic order

Bar Plots

Visualization tool

Frequency plots with bar charts

variable class first_values
Record.number integer 1, 2, 3, 4, 5, 6
Survey.year integer 2020, 2020, 2020, 2020, 2020, 2020
Survey.month character January, January, January, January, January, January
Labour.force.status character Not in labour force, Employed, at work, Not in labour force, Employed, at work, Employed, at work, Employed, at work
Province character Ontario, British Columbia, British Columbia, Ontario, Quebec, Ontario
Census.metropolitan.area integer 0, 0, 9, 0, 0, 3
Age.group character 65-69, 60-64, 70 and over, 45-49, 35-39, 30-34
Sex integer 2, 2, 1, 2, 2, 2
Highest.educational.attainment character Postsecondary certificate or diploma, Above bachelor’s degree, 0 to 8 years, Bachelor’s degree, Postsecondary certificate or diploma, Bachelor’s degree
Single.or.multiple.jobholder character NA, Single jobholder, NA, Single jobholder, Single jobholder, Single jobholder
Class.of.worker..main.job. character NA, private sector employees, NA, public sector employees, private sector employees, private sector employees
Type.of.work..main.job. character NA, Part-time, NA, Full-time, Full-time, Full-time
Occupation.at.main.job character NA, Sales and service, NA, Management, Manufacturing and utilities, Business, finance and administration
Usual.hours.worked.wk.at.main.job double NA, 25, NA, 37.5, 40, 33.7
Actual.hours.worked.wk.at.main.job double NA, 10, NA, 30, 42, 27.7
Duration.of.unemployment integer NA, NA, NA, NA, NA, NA
Reason.for.part.time.work character NA, Personal preference, NA, NA, NA, NA
Reason.for.leaving.job character NA, NA, NA, NA, NA, NA
Usual.hourly.wages..employees.only. double NA, 15, NA, 53.33, 23, 36.52
Job.permanency..employees.only. character NA, Permanent, NA, Permanent, Permanent, Permanent
Flows.into.unemployment character NA, NA, NA, NA, NA, NA
Student.status character NA, Non-student, NA, Non-student, Non-student, Non-student
Statistical.Weight integer 279, 235, 201, 217, 93, 696

Education Count
0 to 8 years 17299
Above bachelor’s degree 26337
Bachelor’s degree 56276
High school graduate 72725
Postsecondary certificate or diploma 123358
Some high school 42341
Some postsecondary 22218

Labour Force Suvey Data | Education

Labour Force Suvey Data | Education

Labeling Options

Bar Chart Design Considerations

Bar Chart Variations - Stacked & Side by Side

Numeric Data

Discrete = Counted

Continuous = Measured

Numeric Data

Interval = Greater or less than

Ratio = Percentage more or less

Counts of Numeric Data

Visualization tool

Frequency plots with histograms

Labour Force survey Data | Wages

Binning Options

Histogram Design Considerations

Year GDP.Per.Capita
2019 46194.73
2018 46313.17
2017 45148.55
2016 42322.48
2015 43585.51
2014 50893.45
2013 52652.59
2012 52678.39
2011 52087.45
2010 47448.01
2009 40773.06
2008 46594.45

Misleading Presentations

Misleading Presentations

Dot Plots & Line Graphs

Country Code Year Life.expectency
7775 Peru PER 2016 74.98300
2366 Curacao CUW 2016 77.87317
1731 Cape Verde CPV 2016 72.79800
5609 Lesotho LSO 2016 54.17400
9941 Ukraine UKR 2016 71.47634
9143 Sudan SDN 2016 64.48600
456 Australia AUS 2016 82.50000
228 Angola AGO 2016 61.54700
7889 Poland POL 2016 77.45122
1960 China CHN 2016 76.25200
6889 Nepal NPL 2016 70.25300
2998 Estonia EST 2016 77.73659
4492 Iceland ISL 2016 82.46829
513 Austria AUT 2016 80.89024
3373 France FRA 2016 82.27317
5723 Libya LBY 2016 71.93400
7547 Palestine PSE 2016 73.47300
5974 Madagascar MDG 2016 65.93200
1389 Bulgaria BGR 2016 74.61463
6946 Netherlands NLD 2016 81.50976

Volume vs Points

Visual Order

Time Sequences

Dots & Lines

Longitude..x. Latitude..y. Station.Name Climate.ID Date.Time Year Month Day Data.Quality Max.Temp..C.
-119.4 49.86 KELOWNA EAST 1123984 2000-01-01 2000 1 1 NA -1.0
-119.4 49.86 KELOWNA EAST 1123984 2000-01-02 2000 1 2 NA 3.0
-119.4 49.86 KELOWNA EAST 1123984 2000-01-03 2000 1 3 NA 0.0
-119.4 49.86 KELOWNA EAST 1123984 2000-01-04 2000 1 4 NA 4.5
-119.4 49.86 KELOWNA EAST 1123984 2000-01-05 2000 1 5 NA 5.0
-119.4 49.86 KELOWNA EAST 1123984 2000-01-06 2000 1 6 NA 0.5
-119.4 49.86 KELOWNA EAST 1123984 2000-01-07 2000 1 7 NA 2.5
-119.4 49.86 KELOWNA EAST 1123984 2000-01-08 2000 1 8 NA 6.0
-119.4 49.86 KELOWNA EAST 1123984 2000-01-09 2000 1 9 NA 4.0
-119.4 49.86 KELOWNA EAST 1123984 2000-01-10 2000 1 10 NA 2.5
-119.4 49.86 KELOWNA EAST 1123984 2000-01-11 2000 1 11 NA 0.5
-119.4 49.86 KELOWNA EAST 1123984 2000-01-12 2000 1 12 NA 3.0

Misleading Presentations

Misleading Presentations

Misleading Presentations

Layering Data and Statistics

Country Code Year Population Continent Life.Expectency GDP
Lesotho LSO 2015 2059000 Africa 51.038 2954
Armenia ARM 2015 2926000 Asia 74.467 9552
Uruguay URY 2015 3412000 South America 77.369 19668
Slovakia SVK 2015 5436000 Europe 76.827 25896
Bosnia and Herzegovina BIH 2015 3429000 Europe 76.865 10305
Mali MLI 2015 17439000 Africa 57.509 1563
Romania ROU 2015 19925000 Europe 75.476 20549
Denmark DNK 2015 5689000 Europe 80.475 44939
Jamaica JAM 2015 2891000 North America 74.098 7115
Senegal SEN 2015 14578000 Africa 66.747 2446
Eswatini SWZ 2015 1104000 Africa 55.359 7726
Pakistan PAK 2015 199427008 Asia 66.577 5056
Gambia GMB 2015 2086000 Africa 60.910 1948
Haiti HTI 2015 10696000 North America 62.485 1649
Vietnam VNM 2015 92677000 Asia 75.110 5733
Congo COG 2015 4856000 Africa 63.097 4526
Gabon GAB 2015 1948000 Africa 64.913 14315
Nepal NPL 2015 27015000 Asia 69.515 2607
North Macedonia MKD 2015 2079000 Europe 75.406 13586
Cyprus CYP 2015 1161000 Europe 80.350 25903

Country Code Year Population Continent Life.Expectency GDP
Lesotho LSO 2015 2059000 Africa 51.038 2954
Armenia ARM 2015 2926000 Asia 74.467 9552
Uruguay URY 2015 3412000 South America 77.369 19668
Slovakia SVK 2015 5436000 Europe 76.827 25896
Bosnia and Herzegovina BIH 2015 3429000 Europe 76.865 10305
Mali MLI 2015 17439000 Africa 57.509 1563
Romania ROU 2015 19925000 Europe 75.476 20549
Denmark DNK 2015 5689000 Europe 80.475 44939
Jamaica JAM 2015 2891000 North America 74.098 7115
Senegal SEN 2015 14578000 Africa 66.747 2446
Eswatini SWZ 2015 1104000 Africa 55.359 7726
Pakistan PAK 2015 199427008 Asia 66.577 5056
Gambia GMB 2015 2086000 Africa 60.910 1948
Haiti HTI 2015 10696000 North America 62.485 1649
Vietnam VNM 2015 92677000 Asia 75.110 5733
Congo COG 2015 4856000 Africa 63.097 4526
Gabon GAB 2015 1948000 Africa 64.913 14315
Nepal NPL 2015 27015000 Asia 69.515 2607
North Macedonia MKD 2015 2079000 Europe 75.406 13586
Cyprus CYP 2015 1161000 Europe 80.350 25903

Country Code Year Population Continent Life.Expectency GDP
Lesotho LSO 2015 2059000 Africa 51.038 2954
Armenia ARM 2015 2926000 Asia 74.467 9552
Uruguay URY 2015 3412000 South America 77.369 19668
Slovakia SVK 2015 5436000 Europe 76.827 25896
Bosnia and Herzegovina BIH 2015 3429000 Europe 76.865 10305
Mali MLI 2015 17439000 Africa 57.509 1563
Romania ROU 2015 19925000 Europe 75.476 20549
Denmark DNK 2015 5689000 Europe 80.475 44939
Jamaica JAM 2015 2891000 North America 74.098 7115
Senegal SEN 2015 14578000 Africa 66.747 2446
Eswatini SWZ 2015 1104000 Africa 55.359 7726
Pakistan PAK 2015 199427008 Asia 66.577 5056
Gambia GMB 2015 2086000 Africa 60.910 1948
Haiti HTI 2015 10696000 North America 62.485 1649
Vietnam VNM 2015 92677000 Asia 75.110 5733
Congo COG 2015 4856000 Africa 63.097 4526
Gabon GAB 2015 1948000 Africa 64.913 14315
Nepal NPL 2015 27015000 Asia 69.515 2607
North Macedonia MKD 2015 2079000 Europe 75.406 13586
Cyprus CYP 2015 1161000 Europe 80.350 25903

Country Code Year Population Continent Life.Expectency GDP
Lesotho LSO 2015 2059000 Africa 51.038 2954
Armenia ARM 2015 2926000 Asia 74.467 9552
Uruguay URY 2015 3412000 South America 77.369 19668
Slovakia SVK 2015 5436000 Europe 76.827 25896
Bosnia and Herzegovina BIH 2015 3429000 Europe 76.865 10305
Mali MLI 2015 17439000 Africa 57.509 1563
Romania ROU 2015 19925000 Europe 75.476 20549
Denmark DNK 2015 5689000 Europe 80.475 44939
Jamaica JAM 2015 2891000 North America 74.098 7115
Senegal SEN 2015 14578000 Africa 66.747 2446
Eswatini SWZ 2015 1104000 Africa 55.359 7726
Pakistan PAK 2015 199427008 Asia 66.577 5056
Gambia GMB 2015 2086000 Africa 60.910 1948
Haiti HTI 2015 10696000 North America 62.485 1649
Vietnam VNM 2015 92677000 Asia 75.110 5733
Congo COG 2015 4856000 Africa 63.097 4526
Gabon GAB 2015 1948000 Africa 64.913 14315
Nepal NPL 2015 27015000 Asia 69.515 2607
North Macedonia MKD 2015 2079000 Europe 75.406 13586
Cyprus CYP 2015 1161000 Europe 80.350 25903

Pie Charts

Finding Data

CBC Fatal Police Encounters

Tracking (In)Justice

The CBC data has been critiqued for lacking a transparent methodology. Additionally, policing scholars have noted that while certain practices in the collection of data by the CBC data meet journalistic standards, they may not meet scholarly research standards.

Guiding Questions

Who would be responsible for

Who would have an interest in keeping a record of the data

Data Aggregators

UBC Data Guide

UBC Purchased Data Sets

Purchased and leased data sets are available in a couple of ways:

Statistics Canada